Norms allow us to compare a student's achievement to students in a large sample: a norm group.

After a test is created, it is administered to a large, diverse group of children who make up the norm group. The children's scores are ranked from low to high performance. The scores are then statistically manipulated to form a statistical model called a normal distribution. Test scores, along with many other attributes such as height, weight, etc., form a normal distribution. This means that there are more scores in the middle than at the highest and lowest ends, and the scores are not biased to the high or low end. A normal distribution graphed visually creates the familiar "bell curve".

How are norms used with MAP Growth?

Norms allow us to compare students to other similar students. We can group students into percentile ranks to give an idea of how the student is ranked compared to the norm group. See What is a percentile rank? for more information about percentiles. With our large sample of student scores over time, we can also create growth norms, which are norms not just for RIT scores, but for the change in RIT score over time. These growth norms allow us to show growth projections on student reports. See How do I interpret a student's growth projection? What does it mean? for more information about growth projections.